|本期目录/Table of Contents|

[1]裘志军.,陈旨娟.,田 心.基于Granger因果分析研究颞叶癫痫脑电的网络特性[J].天津医科大学学报,2014,20(06):437-440.
　QIU Zhi-jun,CHEN Zhi-juan,TIAN Xin.Research on causal network characteristics of EEGs recording of temporal lobe epilepsy patients by domain granger causality analysis[J].Journal of Tianjin Medical University,2014,20(06):437-440.
点击复制

基于Granger因果分析研究颞叶癫痫脑电的网络特性(PDF)
分享到：

《天津医科大学学报》[ISSN:1006-8147/CN:12-1259/R]

卷:
20卷
期数:
2014年06期
页码:
437-440
栏目:
基础医学
出版日期:
2014-11-20

文章信息/Info

Title:
Research on causal network characteristics of EEGs recording of temporal lobe epilepsy patients by domain granger causality analysis
文章编号:
1006-8147（2014）06-0437-04
作者:
裘志军1.2; 陈旨娟1.2; 田 心1
(1.天津医科大学生物医学工程学院,天津300070;2.天津医科大学总医院神经内科,天津300070)
Author(s):
QIU Zhi-jun 1; 2 ; CHEN Zhi-juan1; 2; TIAN Xin1
(1.School of Biomedical Engineering, Tianjin Medical University,Tianjin 300070,China; 2.Department of Neurology, General Hospital, Tianjin Medical University,Tianjin 300070, China)
关键词:

颞叶癫痫; 脑电; 频域Granger因果分析; BC度量值; 因果流

Keywords:

 ; temporal lobe epilepsy;  EEG;  frequency domain Granger causality analysis; Betweenness Centrality;  causal flow

分类号:
R742.1
DOI:
-
文献标志码:
A
摘要:
目的：从多通道脑电的功能性连接的角度，研究癫痫过度放电的机制。方法：对16通道EEGs进行频域Granger因果分析γij，平均后得到DTF值。应用γij构建因果网络，计算BC度量值进行K均值聚类分析，分析变化趋势计算发作期因果流值，以及间歇期、发作前期相对应节点的因果流值。结果：δ频段δ频段DTF值间歇期7.340 4±1.962 9，发作前期4.875 5±1.054 1，发作期8.177±1.697 8，正常组2.159 1±0.556 1δ频段活跃节点BC度量值间歇期0.049 9±0.014 9，发作前期0.046 9±0.009 5，发作期0.080±0.020。δ频段发作期活跃节点值0.686 4±0.303 7，间歇期、发作前期相对应的区域值0.149 5±0.135 8、0.1174±0.0648。结论：颞叶癫痫组发作期活跃节点发作期活跃节点属于因果源非活跃节点属于因果汇。
Abstract:
Objective: To analyze the excessive discharge of epilepsy by functional connectivity and causal network character of multi-channel EEGs. Methods: EEGs of 16 channels were analyzed by frequency domain Granger causality. The γij of causal value between two nodes was calculated, and DTF value was obtained by average. Causal network was built to calculate the BC measurements. BC measurements were analyzed by K-means clustering to compare the change of BC measurements. Causal flow value of ictal periods was calculated, and causal flow values of the node corresponding in interictal and preictal period were compared. Results: The advantage of the energy distribution in ictal period of TLE was the delta frequency. The DTF value of delta frequency: interictal period 7.340 4±1.962 9, preictal period 4.875 5 ±1.054 1, ictal period 8.177±1.697 8, the normal group 2.159 1±0.556 1. Active nodes’ BC measurements of delta frequency: interictal period 0.049 9±0.014 9, preictal period 0.046 9± 0.009 5, ictal period 0.080±0.020. Active node’s causal flow value of delta frequency: ictal period 0.686 4±0.303 7, interictal and preictal period of the corresponding region: 0.1495±0.1358, 0.117 4±0.064 8. Conclusion: The connecting function of TLE group is significantly enhanced, as compared to the normal control group. BC of active nodes in ictal period of TLE significantly increases, as compared to interictal and preictal period. Active nodes in ictal period of TLE are the causal source while inactive nodes in ictal period of TLE are the causal sinks.

参考文献/References:

[1]Christian E E, Christoph H, Martin K. Chronic epilepsy and cognition[J]. The Lancet Neurology, 2004, 3(11):663
[2]Sysoeva M V, Sitnikova E, Sysoev I V, et al. Application of adaptive nonlinear Granger causality: disclosing network changes before and after absence seizure onset in a genetic rat model[J]. J Neurosci Methods, 2014, 226:33
[3]Lu Y F, Yang L, Worrell G A, et al. Seizure source imaging by means of FINE spatio-temporal dipole localization and directed transfer function in partial epilepsy patients[J]. Clin Neurophysiol, 2012, 123(7):1275
[4]Gilsing V, Nooteboom B, Vanhaverbeke W, et al. Network embeddedness and the exploration of novel technologies: Technological distance, betweenness centrality and density[J]. Res Policy, 2008, 37(10):1717
[5]Wilke C, Drongelen W V, Kohrman M. Neocortical seizure foci localization by means of a directed transfer function method[J]. Epilepsia, 2010, 51(4):564
[6]Babiloni F, Cincotti F, Babiloni C, et al. Estimation of the cortical functional connectivity with the multimodal integration of high-resolution EEG and fMRI data by directed transfer function[J]. Neuroimage, 2005,24(1):118
[7]He B, Yakang D, Laura A. A MATLAB toolbox for mapping and imaging of brain functional connectivity[J]. Neuroscience Methods, 2011, 195(2):261
[8]Newman M J. A measure of betweenness centrality based on random walks[J]. Soc Networks, 2005,27(1):39
[9]Yamashita Y, Harada T, Kuniyoshi Y. Causal flow[J]. IEEE Transactions on Multimedia, 2012, 14(3):619

相似文献/References:

备注/Memo

备注/Memo:

基金项目 国家自然科学基金资助项目（91132722）

作者简介 裘志军（1982-），男，硕士在读，研究方向：神经工程；通信作者：田心，E-mail：tianx@tmu.edu.cn。

更新日期/Last Update: 2014-11-21